DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%

DSpark can make decoding faster, but acceptance quality still determines how much speed the system actually realizes.
Semiconductor Engineering

HW-SW Co-Designed System With 3 Core Optimization Pathways For Long-Context Agentic LLM Inference (Cambridge, ICL)

A new technical paper titled “Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference” was published by researchers at University of Cambridge, Imperial College London ...
VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...
diginomicaOpinion

Why context isn't enough - AI agents need real-time organizational truth

I've spent the last year pressing vendors on the problem of context. AI agents need more: they need real-time organization ...
Semiconductor Engineering

AI Inference Needs A Mix-And-Match Memory Strategy

Interactive LLMs (chat, copilots, agents) with strict latency targets Long‑context reasoning (codebases, research, video) with massive KV (key value) cache footprints Ranking and recommendation models ...
Tech Times

NVIDIA Diffusion LLM Hits 2.42x Throughput Without Retraining: Nemotron TwoTower Released

NVIDIA diffusion language model Nemotron TwoTower achieves 2.42x LLM inference throughput without a full retraining run, ...
GIGAZINE

'mesh-llm' allows you to locally run massive AI models by gathering resources from multiple PCs.

Mesh LLM is a mechanism that brings together the surplus GPU computing resources of multiple computers to enable distributed execution of large-scale language models that would be difficult to run on ...